Research Article Zone-Based Routing Protocol for …downloads.hindawi.com/archive/2014/798934.pdf · Zone-Based Routing Protocol for Wireless Sensor Networks ... WSN is more reliable

Research ArticleZone-Based Routing Protocol for Wireless Sensor Networks

Muni Venkateswarlu Kumaramangalam,1

Kandasamy Adiyapatham,1 and Chandrasekaran Kandasamy2

1 Department of Mathematical and Computational Sciences, National Institute of Technology Karnataka, Mangalore 575025, India2Department of Computer Science and Engineering, National Institute of Technology Karnataka, Mangalore 575025, India

Correspondence should be addressed to Muni Venkateswarlu Kumaramangalam; [email protected]

Received 17 June 2014; Accepted 20 October 2014; Published 18 November 2014

Academic Editor: Hyunsung Kim

Copyright © 2014 Muni Venkateswarlu Kumaramangalam et al. This is an open access article distributed under the CreativeCommons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided theoriginal work is properly cited.

Extensive research happening across the globe witnessed the importance ofWireless Sensor Network in the present day applicationworld. In the recent past, various routing algorithms have been proposed to elevate WSN network lifetime. Clustering mechanismis highly successful in conserving energy resources for network activities and has become promising field for researches. However,the problem of unbalanced energy consumption is still open because the cluster head activities are tightly coupled with role andlocation of a particular node in the network. Several unequal clustering algorithms are proposed to solve this wireless sensornetwork multihop hot spot problem. Current unequal clustering mechanisms consider only intra- and intercluster communicationcost. Proper organization of wireless sensor network into clusters enables efficient utilization of limited resources and enhanceslifetime of deployed sensor nodes.This paper considers a novel network organization scheme, energy-efficient edge-based networkpartitioning scheme, to organize sensor nodes into clusters of equal size. Also, it proposes a cluster-based routing algorithm, calledzone-based routing protocol (ZBRP), for elevating sensor network lifetime. Experimental results show that ZBRP out-performsinterims of network lifetime and energy conservation with its uniform energy consumption among the cluster heads.

1. Introduction

Wireless sensor network (WSN) is a distributed collectionof resource constrained tiny nodes capable of operating withminimal user attendance. Rapid development in microelec-tromechanical systems (MEMS) technology has providedsmall sized, low-power, and low-cost sensor nodes with thecapability to sense various types of physical and environ-mental conditions. WSN improves the ability of humans tomonitor and control physical locations from faraway places[1]. Since each sensor node works independently withoutany central control, failure of some nodes does not affectother network activities. WSN is more reliable and securewhen compared to other existing types of networks. WSNis the backbone for establishing smarter environment. Eachsensor node is equipped with one or more low poweredsensors, a processor, memory, a power supply, a radio, andan actuator [2]. Based on infrastructure, WSNs are of twotypes: structured WSNs and unstructured WSNs. Nodes

are deployed in predetermined way in structured WSN,whereas in unstructured WSN they are randomly deployed.Usually, structuredWSN has densely deployed sensor nodes,which are not easily manageable and unstructured WSN willhave limited number of sensor nodes which can be easilymanaged [3]. Sensor node battery is neither replaceable norrechargeable, so the energy conservation has become majorchallenge for energy-constrained WSN. It has the ability towork without any human intervention. Sensor networks areused in many application areas, like military, agriculture,industry, target tracking, data collection, rescue missions,national security, monitoring disaster prone areas, managinginventories, health care, home security, and environmentalstudies [4, 5].

Limited energy resources of wireless sensor networkshould be used wisely to prolong sensor node’s lifetime.To achieve high energy efficiency and increase the networklifetime, sensor nodes are grouped together to form clusters.Each cluster consists of sensor nodes within the given

Hindawi Publishing CorporationInternational Scholarly Research NoticesVolume 2014, Article ID 798934, 9 pageshttp://dx.doi.org/10.1155/2014/798934

2 International Scholarly Research Notices

range. Every cluster would have a leader, often referred toas cluster head (CH), and the other sensor nodes becomecluster members (CMs) of that cluster. A CH may beelected by the sensors in a cluster or preassigned by networkdesigner. Clustering technique has numerous advantages, itcan localize the route setup, conserves communication band-width, avoids redundantmessages exchange, cuts on topologymaintenance overhead, implements optimized managementstrategies to enhance network operations, schedules activitiesin the cluster, prevents medium access collision by limitingredundancy in coverage, and decreases the number of relayedpackets by aggregating data collected by sensors in thenetwork [6]. Sensor nodes (cluster members) within thecluster transmit the sensed data to their cluster head, whichin turn forwards the aggregated data to the sink node.This communication happens either directly or via multihoppath through other cluster heads. In a clustered network,network traffic is composed of intra- and intercluster traffic.Intracluster communication can be single-hop or multihop,similarly intercluster communication also. Previous researchhas shown that multihop communication between the sourceand destination is more energy efficient than direct or single-hop communication [7] due to wireless characteristics. How-ever, the hierarchical (clustering) paradigm causes unevenenergy consumption between cluster heads (interclustercommunication) and cluster members (intracluster com-munication). To balance this energy expenditure, previousresearch proposes cluster head rotation mechanism. Thistechnique balances the energy consumption between CHsand its members but not between the CHs in interclustermultihop communication. Cluster heads close to sink nodedrain their energy faster due to heavy relay traffic and willdie sooner than the other cluster heads.This causes reductionin network coverage and network partitioning. This is calledhot-spot problem in WSN. To solve this problem severalunequal clustering techniques are proposed in the literature[7–12] to balance the energy consumption between the clusterheads. In unequal clustering technique, clusters close to sinknode are smaller in size than those are farther away.Thus, thecluster heads close to base station (Sink) can preserve someenergy for intercluster communication.

Organizing wireless sensor networks into clusters enablesthe efficient utilization of the limited energy resources ofdeployed sensor nodes. However, the problem of unbalancedenergy consumption exists, and it is tightly bound to therole and the location of a particular node in the network[11]. Although it is important to continue pursuing novelalgorithms and protocols to squeeze the most out of theexisting design space, it is equally important to explore othernew design paradigms for future sensor networks. Researchin the recent past focused on exploring resource abundantand unconstrained energy sources exist inWSN. Base station(BS) is one such resource rich component inWSN. Propertieslike resource abundance, unlimited processing capability,huge computational ability, bulky memory, physical locationaccessibility, and, and so forth made BS as a powerful assetforWSN.This approach taps into capabilities at the edge basestation, which has not been fully exploited in prior efforts.This expanded design space has the potential to simplify

various algorithms and protocols on a sensor node, thusoffering new possibilities to drive down the size and cost ofsensor nodes [13]. Several edge-based routing protocols [13–15] are proposed in the literature to shift control overheadburden from sensor node to BS.

In this paper, we consider a new edge-based designspace proposed at [16], for sensor networks with the aim ofconserving energy resource of a sensor node. We propose azone-based routing protocol (ZBRP) for WSN based on theconsidered novel design space. ZBRP considers the featuresof unequal clustering and edge-based routing capabilitiesfor better network resource utilization. It aims to achievebalanced energy consumption in both intra- and interclustercommunication and extends network lifetime by reducingoverall communication cost in the network.

2. Related Work

Many clustering algorithms and cluster based routing proto-cols are proposed for WSN. In the recent past, many algo-rithms are proposed towards effective data communicationand data processing with optimal resource usage in theWSN.In this section, we review some of the most effective routingalgorithms of WSN.

Low energy adaptive clustering hierarchy (LEACH) [17]is one of the most popular distributed cluster-based routingprotocols for WSN. Each node has a certain probability tobecome cluster head per round, and the task of being a clusterhead is rotated among the nodes. LEACH is highly successfulin distributing load uniformly across the network. But its sin-gle hop routing does not serve the requirement of real worldapplications. Also, the randomized CH selection method ofLEACH fails to maintain uniform load distribution betweenthe cluster heads.

Lindsey and Raghavendra introduced a chain-based clus-tering routing protocol, PEGASIS [18]. This is considered asan improvement over LEACH routing protocol. The mainaim of PEGASIS is to minimize the intracluster commu-nication overhead of LEACH protocol. The key idea ofPEGASIS is to form chains with closed by neighboring nodesusing greedy approach. Each chain chooses a leader nodeto forward data to BS. Like LEACH, PEGASIS is single hoprouting protocol. So, this is not a good choice for large scalenetworks.

To address hot spot problem, Li et al. introduced anunequal clusteringmechanism, energy efficient unequal clus-tering (EEUC) [9] to balance energy consumption betweenthe cluster heads. EEUC form small clusters near base stationand the size increases as the distance progress. Thus the clus-ter heads close to base station preserve energy for interclustercommunication. The author also proposed an energy awaremultihop routing protocol for intercluster communication inEEUC mechanism.

Lee et al. have proposed another unequal clusteringalgorithm, energy-efficient distributed unequal clustering(EEDUC) [10] to create distributed clusters inWSN. EEDUCis an extension to EEUC [9] mechanism. Here also, clusterscloser to the base station have smaller size than those farther

International Scholarly Research Notices 3

away from the base station. It considers relay traffic forselecting forwarding CH to forward data towards BS.

Soro and Heinzelman proposed unequal clustering size(UCS) network organization model for WSN [11]. The mainaim of UCS is to enhance the network lifetime by distributingthe load uniformly among the CHs, whose positions arepredetermined. Having BS at center of the network, the CHsare arranged symmetrically in concentric circles in two levelscalled Layers. Respective clusters in their respective layers areof same size and shape with CHs at center. But, the clustersize and shape differ from layer to layer. The aggregateddata from CHs will be delivered to BS through CH to CHcommunication.

Bai et al. introduced multihop clustering algorithm,power-efficient zoning clustering algorithm for WSN(PEZCA) [12], to extend network lifetime by minimizingenergy consumption. It is developed based on two mostpopular clustering protocols, low-energy adaptive clusteringhierarchy (LEACH) [17] and power-efficient gathering insensor information systems (PEGASIS) [18]. PEZCA dividesits network into fan-shaped regions placing BS at center.Each region is considered as a cluster. CH to CH datacommunication delivers data to BS.

Mao andHouhave introduced a novel edge-based routingprotocol, called BeamStar [13] for WSNs. The aim of Beam-Star is to reduce size and cost of the sensor node.This protocolutilizes infrastructure potential provided by an edge basednodes to carry out the network operations. It is assumedthat the network is equipped with a directional antenna withpower control capabilities. Using this, BS can reach any partof the network to provide control information to sensor nodesby varying its transmission power level and beam width.This shifts the control and network management overheadburden from sensor nodes to BS. The power controlledcapability base station scans the complete network withdifferent power transmission levels (sector number (SN))in different angles (ring number (RN)) to provide locationinformation for the nodes. With this location information,sensor nodes can enroute sensed data to BS using controlledbroadcasting mechanism. The data is forwarded by usingsimple forwarding rules provided by BS.

Chen et al. proposed a routing protocol for edge-basedWSNs, called CHIRON [14]. It is developed based on one ofthe most popular hierarchical routing protocols, PEGASIS[18]. Also, it uses the same technique of BeamStar [13]to provide location information for the nodes in the net-work. It outperforms BeamStar with respect to delay timeand network lifetime. CHIRON operates in four differentphases. First phase is Group construction phase, where thesensing field is divided into smaller groups using BeamStarmethodology. The nodes with same Ids form groups. Chainformation phase is the second phase. Here, PEGASIS chainformation process is used to construct smaller chains. Leadernode election phase is the next phase in CHIRON. Nodewith maximum residual energy is elected as “Leader node”for the current round. Cluster Head (CH) to Cluster Headcommunication delivers data to destination node (BS). CHselection process repeats in round robin fashion. The lastphase is data collection and transmission phase. In this phase,

whenever an event occurs, the sensor nodes sense the dataform their surroundings. The sensed data will be collectedand aggregated by chain leader. The same is forwarded toBS using multihop, leader-by-leader communication. TheCHIRON data transmission process is similar to that ofPEGASIS [18] protocol.

To overcome the drawbacks of BeamStar [13], Li andYangproposed a routing protocol for edge-based WSNs, Cluster-based BeamStar (CBS) [15]. CBS also uses the same conceptof BeamStar to provide location information for sensor nodeswith refined sensing process. CBS outperforms BeamStarin efficient usage of power, internode communication, andscan time. CBS protocol is explained in three phases. In thefirst phase, locating phase, sensing field is scanned usingBeamStar mechanism by adjusting the transmission powerlevel. The second phase is, cluster building up phase. Hereit forms clusters with nodes having same Ids. The nodewith maximum residual energy is elected as cluster head(CH), just like in CHIRON [14]. Data transmission is thelast phase in CBS. It uses LEACH [17] protocol to carry outdata transmission process. In this phase, CH aggregates thedata from the cluster members and forwards the same to BSvia intercluster head transmission. New round starts with anadvertisement if CHs energy falls below the given threshold.The cluster member with greater residual energy announcesitself as a new cluster head for the current round.

3. Zone-Based Routing Protocol

Based on two hard-core mechanisms, clustering and networkdesign space, this paper proposes zone-based routing proto-col (ZBRP) for wireless sensor networks. The main aim ofZBRP is to elevate sensor network lifetime by minimizingtotal energy consumption with limited control overhead onsensor nodes in the network. ZBRP creates even size clusterswith minimum control overhead on sensor nodes using theirlocation information. It uses random back-off timers to selectcluster heads in each data forwarding round in the networkfield. Using simple and realistic multihop data forwardingmodel, ZBRP achieves hot-spot free sensor network withuniform energy consumption among cluster heads.

3.1. Network Model. This paper considers a sensor networkwith 𝑛 sensor nodes deployed uniformly over a circular filedwithin the radius𝑅.The base station is located at center of thenetwork to manage and collect data from the network field.The following assumptions are made before we discuss theproposed work.

(1) All the sensor nodes are homogeneous with samecapabilities.

(2) Nodes are not equippedwithGPS (Global PositioningSystem) capable unit and location unaware.

(3) Every node is capable to change its transmissionpower level depends on the distance to the receiver.

(4) Network has continuous data to send.(5) Links are symmetric. Based on RSSI (Received Signal

Strength Indication), any node can compute the


approximate distance to another node for a giventransmission power.

3.2. Problems. In a cluster-based wireless sensor networks,cluster heads spend their energy for intracluster communi-cation and intercluster communication [7]. The amount ofenergy consumed in intracluster communication depends oncluster size. Energy consumption rises with number of sensornodes in a cluster.Themajority of clustering algorithms fromliterature create even size clusters. Cluster heads of evensize clusters tend to consume uniform amount of energyfor intracluster communication [9]. Since sensor nodes havelimited transmission range, they transmit information tosink node using multihop data transmission model. Indata transmission phase, cluster heads act as relay nodesin data forwarding routes to deliver data between sourceand destination. During this inter cluster communicationprocess, the cluster heads closer to the base station areburdened with heavy relay traffic and will die much fasterthan far away cluster heads. This raises hot spot problem andreduces network lifetime. To overcome hot-spot problem,unequal clustering technique [9, 10, 12] is proposed in theliterature. Unequal clustering mechanism creates uneven sizeclusters in the network where size of the cluster increaseswith base station distance. The idea behind creating smallerclusters near BS is to preserve some energy for inter-clustercommunication. Unequal clustering mechanism avoids hotspot problem but introduces several other problems intothe network. It is successful in achieving uniform energydissipation amongCHs but not between clustermembers andcluster heads.

Problems with unequal clustering technique are listedbelow.

(1) Since cluster size rises with base station distance,unequal clustering creates huge clusters as the net-work size increases and leads to coverage and connec-tivity issues in the network.

(2) Unequal clusteringmechanism creates different num-ber of clusters in irregular sizes as there is no con-trol on percentage of CHs it selects in each datatransmission round which causes imbalanced energyconsumption among sensor nodes in the network.

(3) Cluster head selection process involves high controloverhead.

(4) Dynamic nature in cluster size does not guaranteefully connected network.

To overcome the pitfalls listed above, zone-based routingprotocol (ZBRP) is proposed in this paper. ZBRP combinesthe advantages of equal and unequal clustering techniques toform clusters in the network. Its primary goal is to elevatesensor network’s lifetime by minimizing control overheadand total energy consumption in the network.

3.3. Network Design Space. To organize sensor nodes intoclusters and to provide identities for sensor nodes, this paperconsiders network organization scheme proposed in [16]. It

90∘75∘

60∘

45∘

30∘

15∘

0∘

Z35

Z34

Z33

Z32

Z31

Z23

Z22

Z21Z1

1

5A1

3A1

A1

R2R

3R

Figure 1: Novel network organization mechanism, where 𝑍𝑖𝑗repre-

sents 𝑗th partition of 𝑖th ring, called Zone. From Figure 1, it is notedthat each zone has equal area. These equally spaced zones are usedto form even size clusters in the network. This paper considers bothtraditional (equal) and unequal clustering methods to investigateWSN behavior.

is assumed that the base station in the network is equippedwith power controlled capability directional antenna. Thedirectional antenna provides location identity to every sensornode in the network by varying its transmission power levelin different directions. Using these identities, the sensornodes are organized into hierarchical clusters except thenodes fromfirst ring.This arrangement ismade to avoid relaytraffic burden on first ring CH and the nodes from this ringcommunicate with BS directly. Figure 1 illustrates the energy-efficient network organization considered in this paper.

3.4. Clustering Algorithm. In network initialization phase,base station broadcasts an advertisement to all the sensornodes in the network. Based on received signal strengtheach node calculates its distance with BS [12]. To findneighbor nodes, every sensor node advertises its zone id withtransmission radius 𝑟. Number of signals that a node receivesform its zone members give neighborhood count. In the firstdata forwarding round, each node starts a timer (𝑇) definedas the follows:

𝛽 =

𝑑 (𝑆,BS)NHcount

𝑇 =

1 − 𝛼

𝛽

,

(1)

where 𝛼 is a random value between (0, 1), 𝑑(𝑆,BS) is distancebetween source node 𝑆 and base station BS, and numberof neighborhood nodes NHcount. Parameter 𝛽 helps eachzone to find a sensor node which has grater neighborhoodwith smaller distance to base station. Node which clears the


Startif Rnum = 1𝛼 ← rand (0, 1)

𝛽 ←

𝑑 (𝑆,BS)NHcount

𝑇 ←

(1 − 𝛼)

𝛽else𝑝 ←

𝐸init − 𝐸re

𝑅num − 1

𝑞 ←

𝐸se

𝐸re∗

1 − 𝑝

NHcount

𝑇 ←

1

1 − 𝑞

End if

Start timers T for node SIf T = 0

Call CANCEL TIMERS MSG(Zone Id) from 𝑆Call CH ADV MSG(Zone Id) from 𝑆

End if

On call CANCEL TIMERS MSG(Zone Id) from SIf S.Zone Id = N.Zone Id𝑇← −1

End ifOn Receive CH ADV MSG(Zone Id) from SIf S.Zone Id = N.Zone Id AND T = −1

Send JOIN MSG() from𝑁End if

On Receive JOIN MSG() from NPush𝑁 to S.Cluster MembersIncrement Cluster Member Count

End Function

Pseudocode 1: ZBRP pseudocode.

timer 𝑇 will announce itself as a CH for that zone and theremaining nodes join theCHby sending a joinmessage. Aftercluster formation, each clustermember receives TDMA (timedivision multiple access) slot to send information to its CH,whereas sensor nodes from first ring get TDMA slots formthe base station since they communicate with base stationdirectly. Cluster heads aggregate data received from its clustermembers and forwards it as a single fixed length data packetto downstream CH towards base station.

From second round onwards, each node starts back-offtimer as given in (4) to compete for cluster head position:

𝑝 =

𝐸init − 𝐸re𝑅num − 1

, (2)

𝑞 = (

𝐸se𝐸re) ∗ (1 −

𝑝

NHcount) , (3)

𝑇 =

1

1 − 𝑞

, (4)

where 𝐸re is residual energy, 𝐸init is initial energy, 𝑅num isround number, 𝐸se is node spent energy, and NHcount isnumber of neighbor nodes.

Each sensor node starts its timer using its local informa-tion to compete for cluster head position. For cluster head

competition, sensor nodes use communication cost (𝑝) asa primary parameter. The neighborhood count (NHcount) ofsensor node plays an important role in selecting a clusterheads in the network.This parameter allows ZBRP to choosea sensor node with maximum visibility in each zone to guar-antee good coverage andmakes cluster head selection processdistributive. Sensor node with lower communication costhaving greater coverage scope will clear the timer first. Thismethod helps the network to rotate cluster heads uniformlyamong sensor nodes in a zone-based network. Node whichclears the timer 𝑇 announces itself as CH and other sensornodes stop timers and join the CH. Unlike other clusteringalgorithms [9, 10, 12], the proposed work uses only localinformation to select cluster heads in each data forwardinground to minimize control overhead in the network. In theinitial stage, distance with base station is considered as aprimary parameter and in later stages communication costplays prominent role in identifying distributed cluster headsin the network.

The Pseudocode 1 explains the pseudo code of proposedclustering mechanism and Figure 2 represents the flow chartfor the same.


T ←1

1 − q

𝛼 ← rand (0, 1) p ←Einit − ER

Rnum − 1

q ← (Ese/Ere ) ∗ (1 − p/NHcount )

On timer

Start timer T

Call CANCEL TIMERS MSG (zone Id)

Call CH ADV MSG (zone Id)

JOIN MSG ()

Increment Rnum

Start

No Yes If Rnum = 1

𝛽 ←d(S, BS)NHcount

T ←1 − 𝛼

𝛽

Figure 2: ZBRP flow chart.

3.5. Multihop Communication. Every CH aggregates the datareceived locally and then sends the information to BS viamultihop data forwarding model. CH to CH inter clustercommunication mechanism delivers data to BS. The relayingCH is primarily selected based on its location information[16]. Every cluster head keep track number of messages itrelayed in previous rounds and is used during relay nodeselection process. Prior to each data forwarding round,cluster heads announces their location id, residual energy,distance with base station, and number ofmessages it relayed.During data transmission process, source cluster head utilizesthese details to find its relay node to avoid selecting fewcluster heads often to forward data and promotes invariable

energy consumption among cluster heads in the network.Node from downstream closer to base station having greaterresidual energy with lesser amount of messages forwardedwill be selected as a relay node for current data transmissionprocess. Source cluster head always broadcasts its forwardingmessage with its relaying cluster head id. This indicates othercluster heads to update the message counter of the relayingnode for future relay node selection process. When datareaches second ring, the CHs communicate this informationdirectly to BS, whereas sensor nodes from first ring com-municate sensed data directly to base station. For first ringsensor nodes BS allots TDMA slots in network deploymentphase to receive information directly from them.With proper


0

50

100

150

200

0 100 200 300 400 500 600 700 800 900

Num

ber o

f sen

sor n

odes

aliv

e

Number of data forwarding rounds

ZBRPLEACHPEZCA

(a) 200 nodes

50

100

150

200

250

300

350

400

0 100 200 300 400 500 600 700 800

Num

ber o

f sen

sor n

odes

aliv

e

Number of data forwarding rounds

ZBRPLEACHPEZCA

(b) 400 nodes

Figure 3: Number of sensor nodes alive with number of rounds.

relay cluster head selection process, ZBRP achieves uniformenergy consumption among data forwarding cluster headsand avoids hot spot problem in the network.

4. Simulation Results

In this section, the proposed work is evaluated via simulationusing Castalia Simulator [19].The behavior of proposed workis studied in terms of energy consumption and networklifetime. The proposed work ZBRP is compared with awell-known cluster-based routing protocol LEACH [17] andwith an unequal cluster-based routing protocol PEZCA [12].Simplified radio hardware energy dissipationmodel shown in[17] is used in this paper. The simulation parameters used forexperimental work are given in Table 1.

Figure 3 depicts the number of sensor nodes alive in thenetwork versus number of rounds for LEACH, PEZCA, andZBRP. Figure 3(a) represents the number of live nodes in a200 node network and Figure 3(b) represents the number oflive nodes in a 400 node network with number of data for-warding rounds. From Figure 3, it is noted that the proposedrouting protocol, ZBRP, minimizes energy consumption withits distributed cluster head selection process and helps thesensor nodes live longer than LEACH and PEZCA. Also, itis observed from the figure that the number of sensor nodesdie out is substantial in LEACH and PEZCA, whereas it isgradual in ZBRP.

Figure 4 shows network residual energy of three routingprotocols, LEACH, PEZCA, and ZBRP. From the figure it isevident that ZBRP performs better than LEACH and PEZCAin terms of energy consumption. ZBRP achieves invariableenergy dissipation with its multihop communication mecha-nism among cluster heads across the network and helps thenetwork to conserve its resources.

Figures 5(a) and 5(b) show network lifetime of the threealgorithms when 1% nodes dead in 200 nodes and 400 nodes

Table 1: Simulation parameters.

Parameter ValueSimulation area 100m × 100m & 200m × 200mSimulator Castalia [19]Base station (0, 0)Number of sensor nodes 400 & 200Node deployment type UniformInitial energy 2000 joulesEnergy consumed to transmitor receive (𝐸elec)

50 nJ/bit

Transmit amplifier (𝐸amp) 100 pJ/bit/m2

Data packet size 2000 bitsPacket rate 1 per secondNumber of runs 10Simulation time 1500 secondsRound time 50 secondsRadius (𝑅) 100m & 200m

network, respectively. It is realized from Figure 5 that ZBRPimproves network lifetime compared to LEACH and PEZCA.With its distributed cluster head selection mechanism, ZBRPselects cluster heads uniformly across the zone-based net-work and saves energy of every individual sensor node. TheZBRP multihop communication mechanism enables invari-able energy dissipation among cluster heads and promoteshot spot free network with enhanced sensor network lifetime.

5. Conclusion

In this paper, we introduce a novel energy-efficient cluster-based routing protocol, called zone-based routing protocol,


0

50

100

150

200

0 200 400 600 800

Net

wor

k re

sidua

l ene

rgy

in jo

ules

Number of rounds

LEACHPEZCAZBRP

200 nodes

(a) 200 nodes

LEACHPEZCAZBRP

0

50

100

150

200

0 100 200 300 400 500 600 700 800Number of rounds

400 nodes

Net

wor

k re

sidua

l ene

rgy

(J)

(b) 400 nodes

Figure 4: System residual energy with number of rounds.

0

100

200

300

400

500

600

700

LEACH PEZCA ZBRP

Num

ber o

f dat

a for

war

ding

roun

ds

Routing protocol

(a) 200 nodes

0

100

200

300

400

500

600

700

LEACH PEZCA ZBRP

Num

ber o

f dat

a for

war

ding

roun

ds

Routing protocol

(b) 400 nodes

Figure 5: Network lifetime with number of rounds.

for edge-based WSNs. To avoid hot spot problem in tradi-tional clusteringmethods, unequal clusteringmechanismhasbeen introduced. Though it is a widely accepted solution forhot spot problem, it has scalability issues with varied networksizes. Since proper network design space could solve theinconsistent load distribution, we have investigated differentnetwork organization models to address hot spot problem.Therefore, proper network design space with efficient clus-tering technique could solve the issue. We have proposed azone-based routing protocol for the networkmodel describedin [16]. The design space considered plays prominent role informing equal clusters with proper sensor node distributionacross the network. From the simulation results, it is inferredthat, the proposed routing protocol distributes the load

uniformly with proper cluster formation across the networkand enhances the sensor network lifetime.

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper.

Acknowledgment

The authors would like to thank journal editor for acceptingtheir paper for publication.


References

[1] D. Bhattacharyya, T.-H.Kim, and S. Pal, “A comparative study ofwireless sensor networks and their routing protocols,” Sensors,vol. 10, no. 12, pp. 10506–10523, 2010.

[2] X. Ren and H. Yu, “Multipath disjoint routing algorithm forad hoc wireless sensor networks,” in Proceedings of the 8thIEEE International Symposium on Object-Oriented Real-TimeDistributed Computing (ISORC ’05), pp. 253–256, May 2005.

[3] J. Yick, B. Mukherjee, and D. Ghosal, “Wireless sensor networksurvey,”ComputerNetworks, vol. 52, no. 12, pp. 2292–2330, 2008.

[4] K. Akkaya and M. Younis, “A survey on routing protocols forwireless sensor networks,” Ad Hoc Networks, vol. 3, no. 3, pp.325–349, 2005.

[5] X. Liu, “A survey on clustering routing protocols in wirelesssensor networks,” Sensors, vol. 12, no. 8, pp. 11113–11153, 2012.

[6] A. A. Abbasi andM. Younis, “A survey on clustering algorithmsfor wireless sensor networks,” Computer Communications, vol.30, no. 14-15, pp. 2826–2841, 2007.

[7] T. Liu, Q. Li, and P. Liang, “An energy-balancing clusteringapproach for gradient-based routing in wireless sensor net-works,” Computer Communications, vol. 35, no. 17, pp. 2150–2161, 2012.

[8] S. Lee, H. Choe, B. Park, Y. Song, and C.-K. Kim, “LUCA:an energy-efficient unequal clustering algorithm using locationinformation for wireless sensor networks,” Wireless PersonalCommunications, vol. 56, no. 4, pp. 715–731, 2011.

[9] C. Li, M. Ye, G. Chen, and J. Wu, “An energy-efficient unequalclusteringmechanism for wireless sensor networks,” in Proceed-ings of the IEEE International Conference onMobile Ad-Hoc andSensor Systems (MASS ’05), pp. 597–604, IEEE, Washington,DC, USA, November 2005.

[10] S. Lee, J. Lee, H. Sin, S. Yoo, Y. Lee, and S. Kim, “An energy-efficient distributed unequal clustering protocol for wirelesssensor networks,” World Academy of Science, Engineering andTechnology, vol. 48, pp. 443–447, 2008.

[11] S. Soro and W. B. Heinzelman, “Prolonging the lifetime ofwireless sensor networks via unequal clustering,” in Proceedingsof the 19th IEEE International Parallel andDistributed ProcessingSymposium (IPDPS ’05), pp. 236–243, Washington, D.C., USA,April 2005.

[12] F.-E. Bai, H.-H. Mou, and J. Sun, “Power-efficient zoning clus-tering algorithm for wireless sensor networks,” in Proceedingsof the International Conference on Information Engineering andComputer Science (ICIECS ’09), pp. 1–4, December 2009.

[13] S. Mao and Y. T. Hou, “BeamStar: an edge-based approachto routing in wireless sensor networks,” IEEE Transactions onMobile Computing, vol. 6, no. 11, pp. 1284–1296, 2007.

[14] K.-H. Chen, J.-M. Huang, and C.-C. Hsiao, “CHIRON: anenergy-efficient chain-based hierarchical routing protocol inwireless sensor networks,” in Proceedings of the WirelessTelecommunications Symposium (WTS ’09), pp. 1–5, Prague,Czech Republic, April 2009.

[15] W. H. Li and C. Y. Yang, “A cluster-based data routing forwireless sensor networks,” in Algorithms and Architectures forParallel Processing, vol. 5574 of Lecture Notes in ComputerScience, pp. 129–136, Springer, Berlin, Germany, 2009.

[16] K. Muni Venkateswarlu, A. Kandasamy, and K. Chandrase-karan, “Energy-efficient edge-based network partitioningscheme forwireless sensor networks,” inProceedings of the Inter-national Conference on Advances in Computing,

Communications and Informatics (ICACCI ’13), pp. 1017–1022, 2013.

[17] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan,“Energy-efficient communication protocol for wireless micro-sensor networks,” in Proceedings of the 33rd Hawaii Interna-tional Conference on System Sciences (HICSS ’00), vol. 8, pp.8020–8029, IEEE Computer Society, Washington, DC, USA,2000, http://dl.acm.org/citation.cfm?id=820264.820485.

[18] S. Lindsey and C. S. Raghavendra, “PEGASIS: power-efficientgathering in sensor information systems,” in Proceedings of theIEEE Aerospace Conference, pp. 1125–1130, March 2002.

[19] A. Boulis, Castalia, A Simulator for Wireless Sensor Networksand Body Area Networks, NICTA, Eveleigh, Australia, 2013.

Submit your manuscripts athttp://www.hindawi.com

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttp://www.hindawi.com Volume 2014


Distributed Sensor Networks


Advances in

FuzzySystems

Hindawi Publishing Corporationhttp://www.hindawi.com

Volume 2014


ReconfigurableComputing

Hindawi Publishing Corporation http://www.hindawi.com Volume 2014


Applied Computational Intelligence and Soft Computing

Advances in

Artificial Intelligence


Advances inSoftware EngineeringHindawi Publishing Corporationhttp://www.hindawi.com Volume 2014


Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications


Hindawi Publishing Corporation

http://www.hindawi.com Volume 2014

Advances in

Multimedia


Biomedical Imaging


ArtificialNeural Systems

Advances in


RoboticsJournal of



Computational Intelligence and Neuroscience

Industrial EngineeringJournal of


Modelling & Simulation in EngineeringHindawi Publishing Corporation http://www.hindawi.com Volume 2014

The Scientific World JournalHindawi Publishing Corporation http://www.hindawi.com Volume 2014


Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in


Documents

Research Article Zone-Based Routing Protocol for …downloads.hindawi.com/archive/2014/798934.pdf · Zone-Based Routing Protocol for Wireless Sensor Networks ... WSN is more reliable